Biometrics authentication device and biometrics authentication method

ABSTRACT

A biometrics authentication device is configured to include: a filter that extracts from an input image directional features that respectively correspond to directions different from each other; an perpendicular filter that, from among the directional features extracted from the filter, decreases a luminance value of the entirety of a directional feature that corresponds to a prescribed direction, increases a luminance value of the entirety of a directional feature that corresponds to a direction perpendicular to the directional feature that corresponds to the prescribed direction, and outputs other directional features with no change; a non-directional feature generation processing unit that generates a non-directional feature on the basis of the directional features output from the perpendicular filter; a matching processing unit that obtains a similarity between the non-directional feature and a registered non-directional feature stored in a storing unit; and a determining unit that determines identity by using the similarity.

CROSS-REFERENCE TO RELATED APPLICATION

This application is continuation application of InternationalApplication PCT/JP2014/058386 filed on Mar. 25, 2014 and designated theU.S., the entire contents of which are incorporated herein by reference.

FIELD

Embodiments of the present disclosure relate to a technology forbiometrics authentication.

BACKGROUND

In an existing biometrics authentication device, when biologicalinformation extracted from a photographed image and registeredbiological information match each other, identity is determined. Thebiological information includes features that denote palm prints, veins,and the like, and when biometrics authentication is performed by usingthe feature denoting a vein, the feature denoting a palm print needs tobe separated from the photographed image such that as much as possibleonly the feature denoting a vein is included in the biologicalinformation. As an example of a method for separating the featuredenoting a palm print, a method for optically separating the featuredenoting a palm print by using a polarizing filter is known. As anotherexample of the method, a method using plural-wavelength photographing isknown.

Related Art Document: A. Ross, A. K. Jain, and J. Reisman, “A Hybridfingerprint matcher”, Pattern Recognition, vol. 36, no. 7, pp.1661-1673, 2003.

SUMMARY

A biometrics authentication device according to the embodiments of thepresent disclosure includes: a filter that extracts from an input imagedirectional features that respectively correspond to directionsdifferent from each other; an perpendicular filter that, from among thedirectional features extracted from the filter, decreases a luminancevalue of the entirety of a directional feature that corresponds to aprescribed direction, increases a luminance value of the entirety of adirectional feature that corresponds to a direction perpendicular to thedirectional feature that corresponds to the prescribed direction, andoutputs other directional features with no change; a non-directionalfeature generation processing unit that generates a non-directionalfeature on the basis of the directional features output from theperpendicular filter; a matching processing unit that obtains asimilarity between the non-directional feature and a registerednon-directional feature stored in a storing unit; and a determining unitthat determines identity by using the similarity.

Further, a biometrics authentication device according to the embodimentsof the present disclosure includes: a filter that extracts from an inputimage directional features that respectively correspond to directionsdifferent from each other; a non-directional feature generationprocessing unit that generates a non-directional feature on the basis ofthe directional features extracted from the filter; a selecting unitthat selects a directional feature that corresponds to a prescribeddirection from among the directional features extracted from the filter,and outputs the directional feature as a significant directionalfeature, and that also selects a directional feature that corresponds toa direction perpendicular to the significant directional feature fromamong the directional features extracted from the filter, and outputsthe directional feature as an perpendicular directional feature; anon-directional feature matching processing unit that obtains a firstsimilarity between the non-directional feature and a registerednon-directional feature stored in a storing unit; a directional featurematching processing unit that obtains a second similarity between thesignificant directional feature and a registered significant directionalfeature stored in the storing unit, and that also obtains a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;a similarity adjusting unit that applies a smaller weight on the secondsimilarity than on the third similarity, and outputs a sum of the secondsimilarity and the third similarity after weighting as a fourthsimilarity; and a determining unit that determines identity by using thefirst similarity and the fourth similarity.

Furthermore, a biometrics authentication device according to theembodiments of the present disclosure includes: a filter that extractsfrom an input image directional features that respectively correspond todirections different from each other; a non-directional featuregeneration processing unit that generates a non-directional feature onthe basis of the directional features extracted from the filter; aselecting unit that selects a directional feature that corresponds to aprescribed direction from among the directional features extracted fromthe filter, and outputs the directional feature as a significantdirectional feature, and that also selects a directional feature thatcorresponds to a direction perpendicular to the significant directionalfeature from among the directional features extracted from the filter,and outputs the directional feature as an perpendicular directionalfeature; a non-directional feature matching processing unit that obtainsa first similarity between the non-directional feature and a registerednon-directional feature stored in a storing unit; a directional featurematching processing unit that obtains a second similarity between thesignificant directional feature and a registered significant directionalfeature stored in the storing unit, and that also obtains a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;a similarity adjusting unit that applies a greater weight on the thirdsimilarity than on the second similarity, and outputs a sum of thesecond similarity and the third similarity after weighting as a fourthsimilarity; and a determining unit that determines identity by using thefirst similarity and the fourth similarity.

A biometrics authentication method according to the embodiments of thepresent disclosure includes: extracting from an input image, by acomputer, directional features that respectively correspond todirections different from each other; from among the directionalfeatures that have been extracted, decreasing, by the computer, aluminance value of the entirety of a directional feature thatcorresponds to a prescribed direction, increasing a luminance value ofthe entirety of a directional feature that corresponds to a directionperpendicular to the directional feature that corresponds to theprescribed direction, and outputting other directional features with nochange; generating, by the computer, a non-directional feature on thebasis of the directional features that have been output; obtaining, bythe computer, a similarity between the non-directional feature and aregistered non-directional feature stored in a storing unit; anddetermining, by the computer, identity by using the similarity.

Further, a biometrics authentication method according to the embodimentsof the present disclosure includes: extracting from an input image, by acomputer, directional features that respectively correspond todirections different from each other; generating, by the computer, anon-directional feature on the basis of the directional features thathave been extracted; selecting, by the computer, a directional featurethat corresponds to a prescribed direction from among the directionalfeatures that have been extracted, and outputting the directionalfeature as a significant directional feature; selecting, by thecomputer, a directional feature that corresponds to a directionperpendicular to the significant directional feature from among thedirectional features that have been extracted, and outputting thedirectional feature as an perpendicular directional feature; obtaining,by the computer, a first similarity between the non-directional featureand a registered non-directional feature stored in a storing unit;obtaining, by the computer, a second similarity between the significantdirectional feature and a registered significant directional featurestored in the storing unit; obtaining, by the computer, a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;applying, by the computer, a smaller weight on the second similaritythan on the third similarity, and outputting a sum of the secondsimilarity and the third similarity after weighting as a fourthsimilarity; and determining, by the computer, identity by using thefirst similarity and the fourth similarity.

Furthermore, a biometrics authentication method according to theembodiments of the present disclosure includes: extracting from an inputimage, by a computer, directional features that respectively correspondto directions different from each other; generating, by the computer, anon-directional feature on the basis of the directional features thathave been extracted; selecting, by the computer, a directional featurethat corresponds to a prescribed direction from among the directionalfeatures that have been extracted, and outputting the directionalfeature as a significant directional feature; selecting, by thecomputer, a directional feature that corresponds to a directionperpendicular to the significant directional feature from among thedirectional features that have been extracted, and outputting thedirectional feature as an perpendicular directional feature; obtaining,by the computer, a first similarity between the non-directional featureand a registered non-directional feature stored in a storing unit;obtaining, by the computer, a second similarity between the significantdirectional feature and a registered significant directional featurestored in the storing unit; obtaining, by the computer, a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;applying, by the computer, a greater weight on the third similarity thanon the second similarity, and outputting a sum of the second similarityand the third similarity after weighting as a fourth similarity; anddetermining, by the computer, identity by using the first similarity andthe fourth similarity.

A non-transitory computer-readable recording medium according to theembodiments of the present disclosure which records a program causes acomputer to execute a process including: extracting from an input imagedirectional features that respectively correspond to directionsdifferent from each other; from among the directional features that havebeen extracted, decreasing a luminance value of the entirety of adirectional feature that corresponds to a prescribed direction,increasing a luminance value of the entirety of a directional featurethat corresponds to a direction perpendicular to the directional featurethat corresponds to the prescribed direction, and outputting otherdirectional features with no change; generating a non-directionalfeature on the basis of the directional features that have been output;obtaining a similarity between the non-directional feature and aregistered non-directional feature stored in a storing unit; anddetermining identity by using the similarity.

Further, a non-transitory computer-readable recording medium accordingto the embodiments of the present disclosure which records a programcauses a computer to execute a process including: extracting from aninput image directional features that respectively correspond todirections different from each other; generating a non-directionalfeature on the basis of the directional features that have beenextracted; selecting a directional feature that corresponds to aprescribed direction from among the directional features that have beenextracted, and outputting the directional feature as a significantdirectional feature; selecting a directional feature that corresponds toa direction perpendicular to the significant directional feature fromamong the directional features that have been extracted, and outputtingthe directional feature as an perpendicular directional feature;obtaining a first similarity between the non-directional feature and aregistered non-directional feature stored in a storing unit; obtaining asecond similarity between the significant directional feature and aregistered significant directional feature stored in the storing unit;obtaining a third similarity between the perpendicular directionalfeature and a registered perpendicular directional feature stored in thestoring unit; applying a smaller weight on the second similarity than onthe third similarity, and outputting a sum of the second similarity andthe third similarity after weighting as a fourth similarity; anddetermining identity by using the first similarity and the fourthsimilarity.

Furthermore, a non-transitory computer-readable recording mediumaccording to the embodiments of the present disclosure which records aprogram causes a computer to execute a process including: extractingfrom an input image directional features that respectively correspond todirections different from each other; generating a non-directionalfeature on the basis of the directional features that have beenextracted; selecting a directional feature that corresponds to aprescribed direction from among the directional features that have beenextracted, and outputting the directional feature as a significantdirectional feature; selecting a directional feature that corresponds toa direction perpendicular to the significant directional feature fromamong the directional features that have been extracted, and outputtingthe directional feature as an perpendicular directional feature;obtaining a first similarity between the non-directional feature and aregistered non-directional feature stored in a storing unit; obtaining asecond similarity between the significant directional feature and aregistered significant directional feature stored in the storing unit;obtaining a third similarity between the perpendicular directionalfeature and a registered perpendicular directional feature stored in thestoring unit; applying a greater weight on the third similarity than onthe second similarity, and outputting a sum of the second similarity andthe third similarity after weighting as a fourth similarity; anddetermining identity by using the first similarity and the fourthsimilarity.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of a biometrics authentication deviceaccording to a first embodiment.

FIG. 2 is a flowchart illustrating a biometrics authentication methodaccording to the first embodiment.

FIG. 3 illustrates an example of a feature extracting unit according tothe first embodiment.

FIG. 4 illustrates an example of a matching processing unit according tothe first embodiment.

FIG. 5 illustrates an example of a biometrics authentication deviceaccording to a second embodiment.

FIG. 6 is a flowchart illustrating a biometrics authentication methodaccording to the second embodiment.

FIG. 7 illustrates an example of a feature extracting unit according tothe second embodiment.

FIG. 8 illustrates an example of a matching processing unit according tothe second embodiment.

FIG. 9 illustrates an example of hardware of a biometrics authenticationdevice.

DESCRIPTION OF EMBODIMENTS First Embodiment

FIG. 1 illustrates an example of a biometrics authentication deviceaccording to a first embodiment.

A biometrics authentication device 1 illustrated in FIG. 1 includes animage obtaining unit 2, a region specifying unit 3, a feature extractingunit 4, a matching processing unit 5, a score determining unit 6 (adetermining unit), and a storing unit 7.

FIG. 2 is a flowchart illustrating a biometrics authentication methodaccording to the first embodiment.

First, the image obtaining unit 2 obtains an image of a hand of asubject (S1). As an example, the image obtaining unit 2 is an imagingdevice, and the image obtaining unit 2 obtains a captured image of ahand of a subject by using a single-plate image sensor and respectiveRGB color filters of a Bayer array.

Then, the region specifying unit 3 specifies a ROI (Region Of Interest)that corresponds to a palm of a subject in the image obtained by theimage obtaining unit 2 (S2).

The feature extracting unit 4 then extracts a non-directional featurefrom an image f of the ROI specified by the region specifying unit 3(S3). In a case in which filtering S is performed on an image f, theterm “non-directional” is defined to obtain a result that is almost thesame as a result of performing only the filtering S even when imagerotation conversion T_(θ) or inverse conversion T_(θ) ⁻¹ of T_(θ) onvarious angles θ is inserted before the filtering S. Stated another way,the term “non-directional” is defined using symbols to be S(f)=T_(θ)⁻¹(S(T_(θ)(f))) at an arbitrary angle θ.

Then, the matching processing unit 5 obtains a similarity between thenon-directional feature extracted by the feature extracting unit 4 and aregistered non-directional feature that has been registered in advanceand that has been stored in the storing unit 7 (S4).

The score determining unit 6 determines the identity of the subjectaccording to the similarity obtained by the matching processing unit 5(S5).

FIG. 3 illustrates an example of the feature extracting unit 4 accordingto the first embodiment.

The feature extracting unit 4 illustrated in FIG. 3 includes a filter41, an perpendicular filter 42, a point-wise maximum selecting unit 43,a binarizing unit 44, and a skeletonizing unit 45.

The filter 41 performs Gabor filtering on (luminance values of allpixels of) an input image f of the ROI in eight respective directions θ(0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°) so as to obtainrespective filter responses (luminance values) as directional featuresg_(θ) (a directional feature g_(0°), a directional feature g_(22.5°), adirectional feature g_(45°), a directional feature g_(67.5°), adirectional feature g_(90°), a directional feature g_(112.5°), adirectional feature g_(135°), and a directional feature g_(157.5°)). Thenumber of directions θ set in the filtering is not limited to eight, andmay be any number that is greater than or equal to two. The filtering isnot limited to Gabor filtering, and may be any type of filtering thathas a high filter response to a linear dark portion in respectivedirections θ in the image f.

From among the respective directional features g_(θ) extracted from thefilter 41, the perpendicular filter 42 decreases a luminance value ofthe entirety of the directional feature g_(0°) that corresponds to adirection θ of 0° (a directional feature g_(θ) of an S (Significant)component), increases a luminance value of the entirety of thedirectional feature g_(90°) that corresponds to a directionperpendicular to the directional feature g_(θ) of the S component (adirectional feature g_(θ) of a P (Perpendicular) component), and outputsthe other directional features g_(θ) with no change.

The point-wise maximum selecting unit 43 outputs a non-directionalfeature g on the basis of the respective directional features ge outputfrom the perpendicular filter 49. As an example, the point-wise maximumselecting unit 43 outputs a maximum directional featuremax_(θ){g_(θ)(i,j)} as a non-directional feature g(i,j) from among therespective directional features g_(θ)(i,j) output from the perpendicularfilter 49, as expressed by Expression 1. The i represents a position ina horizontal-axis direction of two-dimensional coordinates, and the jrepresents a position of a vertical-axis direction of thetwo-dimensional coordinates, when positions of all of the pixels withinthe ROI are made to correspond to positions on the two-dimensionalcoordinates.

g(i,j):=max_(θ) {g _(θ)(i,j)},(i,j)εROI  Expression 1

The binarizing unit 44 outputs 1 as a non-directional plane featureb(i,j) when the non-directional feature g(i,j) output from thepoint-wise maximum selecting unit 43 has a positive value, and thebinarizing unit 44 outputs 0 as the non-directional plane feature b(i,j)when the non-directional feature g(i,j) has a value that is not apositive value, as expressed by Expression 2. The obtainednon-directional plane feature b is stored in the storing unit 7.

$\begin{matrix}{{b\left( {i,j} \right)} = \left\{ \begin{matrix}{1,\mspace{14mu} {{{if}\mspace{14mu} {g\left( {i,j} \right)}} > 0}} \\{0,\mspace{14mu} {other}}\end{matrix} \right.} & {{Expression}\mspace{14mu} 2}\end{matrix}$

In the description above, the binarizing unit 44 has performedbinarization by performing simple thresholding using a constant of 0,but the binarizing unit 44 may perform binarization using a moreadvanced Adaptive-thresholding scheme.

The skeletonizing unit 45 performs skeletonizing on the non-directionalplane feature b so as to obtain a non-directional line feature LF, asexpressed by Expression 3. The skel represents skeletonizing. Theobtained non-directional line feature LF is stored in the storing unit7. The line feature is a linear image.

LF:=skel(b)  Expression 3

The matching processing unit 5 illustrated in FIG. 1 obtains asimilarity score between the non-directional line feature LF that hasbeen output from the skeletonizing unit 45 and that has been stored inthe storing unit 7 and a registered non-directional line feature TLFthat has been registered in advance and that has been stored in thestoring unit 7, as illustrated in FIG. 4.

The score determining unit 6 illustrated in FIG. 1 determines theidentity of the subject when the similarity score is greater than orequal to a threshold.

As an example, when a direction perpendicular to a longitudinaldirection of a hand of a subject is 0°, a palm print is primarilyconfigured of lines generated when the hand is clenched, and therefore adirection θ that corresponds to a directional feature g_(θ) that isestimated to include a large portion indicating the palm print is 0°.Accordingly, the directional feature g_(θ) of the S component is adirectional feature g_(θ) whereby a palm print in the ROI has beenemphasized, and a non-directional line feature LF generated by using thedirectional feature g_(θ) of the S component whereby the luminance valueof the entirety decreases is a feature whereby an influence of the palmprint has been suppressed. This allows the identity of the subject to bedetermined in a state in which the influence of the palm print has beensuppressed, and consequently authentication accuracy can be improved.Stated another way, the biometrics authentication device 1 according tothe first embodiment can prevent the FAR from increasing even when amethod for physically separating a feature denoting a palm print from animage fails to be applied. Even when melanin is abnormally deposited, inparticular, in a palm of a subject, and a large portion indicating apalm print is included in a directional feature g_(θ), the identity ofthe subject can be determined in a state in which an influence of thepalm print on the non-directional line feature LF is suppressed, andconsequently the FAR can be reduced.

When a longitudinal direction of a hand of a subject is 90°, a veinprincipally extends in a direction from a wrist to four fingers, andtherefore a direction θ that corresponds to a directional feature g_(θ)estimated to include a large portion indicating the vein is 90°.Accordingly, the directional feature g_(θ) of the P component is adirectional feature g_(θ) whereby the vein within the ROI has beenemphasized, and a non-directional line feature LF generated by using thedirectional feature g_(θ) of the P component whereby the luminance valueof the entirety has increased is a feature whereby the vein has beenemphasized. Consequently, the identity of the subject can be determinedin a state in which a vein that has a higher level of diversity than thepalm print has been emphasized, and therefore the false rejection ratecan be reduced.

Second Embodiment

FIG. 5 illustrates an example of a biometrics authentication deviceaccording to a second embodiment.

A biometrics authentication device 1 illustrated in FIG. 5 includes animage obtaining unit 2, a region specifying unit 3, a feature extractingunit 4, a matching processing unit 5, a score determining unit 6 (adetermining unit), and a storing unit 7.

The feature extracting unit 4 includes a non-directional featuregeneration processing unit 8 and a directional feature generationprocessing unit 9.

The matching processing unit 5 includes a non-directional featurematching processing unit 10 and a directional feature matchingprocessing unit 11.

FIG. 6 is a flowchart illustrating a biometrics authentication methodaccording to the second embodiment.

First, the image obtaining unit 2 obtains an image of a hand of asubject (S11). As an example, the image obtaining unit 2 is an imagingdevice, and the image obtaining unit 2 obtains a captured image of ahand of a subject by using a single-plate image sensor and respectiveRGB color filters of a Bayer array.

Then, the region specifying unit 3 specifies a ROI that corresponds to apalm of the subject in the image obtained by the image obtaining unit 2(S12).

The non-directional feature generation processing unit 8 generates anon-directional feature from an image f of the ROI specified by theregion specifying unit 3, and the directional feature generationprocessing unit 9 generates a directional feature from the image f ofthe ROI specified by the region specifying unit 3 (S13). The term.“directional” is defined to not be non-directional.

The non-directional feature matching processing unit 10 obtains asimilarity between the non-directional feature generated by thenon-directional feature extraction processing unit 8 and a registerednon-directional feature that has been registered in advance and that hasbeen stored in the storing unit 7, and the directional feature matchingprocessing unit 11 obtains a similarity between the directional featuregenerated by the directional feature extraction processing unit 9 and aregistered directional feature that has been registered in advance andthat has been stored in the storing unit 7 (S14).

Then, the score determining unit 6 determines the identity of thesubject according to the similarity obtained by the non-directionalfeature matching processing unit 10 and the similarity obtained by thedirectional feature matching processing unit 11 (S15).

FIG. 7 illustrates an example of the feature extracting unit 4 accordingto the first embodiment. The same components as the componentsillustrated in FIG. 3 are denoted by the same reference numerals, andthe description thereof is omitted.

The feature extracting unit 4 illustrated in FIG. 7 includes a filter41, a point-wise maximum selecting unit 43, a binarizing unit 44, askeletonizing unit 45, a selecting unit 46, and a binarizing unit 47.

The point-wise maximum selecting unit 43 outputs a maximum directionfeature max_(θ){g_(θ)(i,j)} as a non-directional feature g(i,j) fromamong respective directional features g_(θ)(i, j) extracted from thefilter 41, as expressed by Expression 1 above.

The binarizing unit 44 outputs 1 as a non-directional plane featureb(i,j) when the non-directional feature g(i,j) output from thepoint-wise maximum selecting unit 43 has a positive value, and thebinarizing unit 44 outputs 0 as the non-directional plane feature b(i,j)when the non-directional feature g(i,j) has a value that is not apositive value, as expressed by Expression 2 above. The obtainednon-directional plane feature b is stored in the storing unit 7.

The skeletonizing unit 45 performs skeletonizing on the non-directionalplane feature b so as to obtain a non-directional line feature LF, asexpressed by Expression 3 above. The skel represents skeletonizing. Theobtained non-directional line feature LF is stored in the storing unit7.

From among the respective directional features g_(θ) extracted from thefilter 41, the selecting unit 46 selects a directional feature g_(0°)that corresponds to a direction θ of 0°, and outputs the directionalfeature g_(0°) as a significant directional feature g_(s), and theselecting unit 46 also selects a directional feature g_(90°) thatcorresponds to a direction perpendicular to the significant directionalfeature g_(θ), and outputs the directional feature g_(90°) as anperpendicular directional feature g_(p).

The binarizing unit 47 performs binarization on each of the significantdirectional feature g_(s) and the perpendicular directional featureg_(p) selected by the selecting unit 46, and outputs the results as asignificant directional plane feature b_(s) and an perpendiculardirectional plane feature b_(p).

As an example, the binarizing unit 47 outputs 1 as the significantdirectional plane feature b_(s)(i,j) when the significant directionalfeature g_(s)(i,j) is positive, and the binarizing unit 47 outputs 0 asthe significant directional plane feature b_(s)(i,j) when thesignificant directional feature g_(s)(i,j) is not positive, as expressedby Expression 4. The obtained significant directional plane featureb_(s) is stored in the storing unit 7.

$\begin{matrix}{{b_{s}\left( {i,j} \right)} = \left\{ \begin{matrix}{1,\mspace{14mu} {{{if}\mspace{14mu} {g_{s}\left( {i,j} \right)}} > 0}} \\{0,\mspace{14mu} {other}}\end{matrix} \right.} & {{Expression}\mspace{14mu} 4}\end{matrix}$

In addition, the binarizing unit 47 outputs 1 as the perpendiculardirectional plane feature b_(p)(i,j) when the perpendicular directionalfeature g_(p)(i,j) is positive, and the binarizing unit 47 outputs 0 asthe perpendicular directional plane feature b_(p)(i,j) when theperpendicular directional feature g_(p)(i,j) is not positive, asexpressed by Expression 5.

The obtained perpendicular directional plane feature b_(p) is stored inthe storing unit 7.

$\begin{matrix}{{b_{p}\left( {i,j} \right)} = \left\{ \begin{matrix}{1,\mspace{14mu} {{{if}{\mspace{11mu} \;}{g_{p}\left( {i,j} \right)}} > 0}} \\{0,\mspace{14mu} {other}}\end{matrix} \right.} & {{Expression}\mspace{14mu} 5}\end{matrix}$

In the description above, the binarizing unit 47 has performedbinarization by performing simple thresholding using a constant of 0,but the binarizing unit 47 may perform binarization using a moreadvanced Adaptive-thresholding scheme.

FIG. 8 illustrates an example of the matching processing unit 5according to the second embodiment.

The matching processing unit 5 illustrated in FIG. 8 includes anon-directional feature matching processing unit 10, a directionalfeature matching processing unit 11, and a similarity adjusting unit 51.

The non-directional feature matching processing unit 10 obtains asimilarity score₁ between the non-directional line feature LF that hasbeen output from the skeletonizing unit 45 and that has been stored inthe storing unit 7 and a registered non-directional line feature TLFthat has been registered in advance and that has been stored in thestoring unit 7.

The directional feature matching processing unit 11 obtains a similarityscore₂ between the significant directional plane feature b_(s) that hasbeen output from the binarizing unit 47 and that has been stored in thestoring unit 7 and a registered significant directional plane featureTb_(s) that has been registered in advance and that has been stored inthe storing unit 7, and the directional feature matching processing unit11 also obtains a similarity score₃ between the perpendiculardirectional plane feature b_(p) that has been output from the binarizingunit 47 and that has been stored in the storing unit 7 and a registeredperpendicular directional plane feature Tb_(p) that has been registeredin advance and that has been stored in the storing unit 7.

The similarity adjusting unit 51 weights the similarities score₂ andscore₃ output from the directional feature matching processing unit 11by using a constant a_(k) and a constant c, and the similarity adjustingunit 51 outputs the sum of the weighted similarities score₂ and score₃as a similarity score₄, as expressed by Expression 6.

$\begin{matrix}{{score}_{4}:={{\sum\limits_{{2 \leq k \leq 3}\;}^{\;}\; {a_{k}*{score}_{k}}} + c}} & {{Expression}\mspace{14mu} 6}\end{matrix}$

Hereinafter, with respect to a certain k, when a_(k)>0 is establishedand as an absolute value of the constant a_(k) becomes relativelygreater than another constant a_(k), it is said that score_(k) ispositively utilized (an action of positively affirming similarity).Otherwise, it is said that score_(k) is negatively utilized.

The similarity adjusting unit 51 applies a smaller weight on thesimilarity score₂ than on the similarity score₃ so as to negativelyutilize the similarity score₂.

The similarity adjusting unit 51 applies a greater weight on thesimilarity score₃ than on the similarity score₂ so as to positivelyutilize the similarity score₃.

Then, the score determining unit 6 illustrated in FIG. 1 determines thatthe subject is a person to be authenticated when a similarity score thatis the sum of the similarity score₁ and the similarity score₄ is greaterthan or equal to a threshold.

As described above, by negatively utilizing the similarity score₂, theidentity of a subject can be determined in a state in which an influenceof a palm print has been suppressed, and authentication accuracy can beimproved. Stated another way, the biometrics authentication device 1according to the second embodiment can prevent the FAR from increasingeven when a method for physically separating a feature denoting the palmprint from an image fails to be applied. Further, even when melanin isabnormally deposited, in particular, in a palm of a subject, and a largeportion indicating the palm print is included in a non-directional linefeature LF, the identity of the subject can be determined in a state inwhich an influence of the palm print on the non-directional line featureLF has been suppressed, and consequently the FAR can be reduced.

Further, by positively utilizing the similarity score₃, the identity ofa subject can be determined in a state in which a vein that has a higherlevel of diversity than the palm print has been emphasized, andtherefore the false rejection rate can be reduced.

FIG. 9 illustrates an example of hardware configuring the biometricsauthentication device 1 according to the embodiments of the presentdisclosure.

As illustrated in FIG. 9, the hardware configuring the biometricsauthentication device 1 includes a control unit 1201, a storing unit1202, a recording medium reading device 1203, an input/output interface1204, and a communication interface 1205, and these components aremutually connected via a bus 1206. The hardware configuring thebiometrics authentication device 1 may be implemented by using a cloudor the like.

As an example, a Central Processing Unit (CPU), a multicore CPU, or aprogrammable device (a Field Programmable Gate Array (FPGA), aProgrammable Logic Device (PLD) or the like) can be considered to beused as the control unit 1201.

The control unit 1201 corresponds to the region specifying unit 3, thefeature extracting unit 4, the matching processing unit 5, and the scoredetermining unit 6 illustrated in FIG. 1 or 5.

The storing unit 1202 corresponds to the storing unit 7 illustrated inFIG. 1 or 5, and as an example, a memory such as a Read Only Memory(ROM) or a Random Access Memory (RAM), or a hard disk can be consideredto be used as the storing unit 1202. The storing unit 1202 may be usedas a work area at the time of execution. In addition, another storingunit may be provided outside the biometrics authentication device 1.

The recording medium reading device 1203 is controlled by the controlunit 1201 so as to read data recorded in a recording medium 1207 or towrite data to the recording medium 1207. The recording medium 1207 thatis removable is a non-transitory computer-readable recording medium, andexamples of the recording medium 1207 include a magnetic recordingmedium, an optical disk, a magneto-optical recording medium, and asemiconductor memory. Examples of the magnetic recording device includea hard disk drive (HDD). Examples of the optical disk include a DigitalVersatile Disc (DVD), a DVD-RAM, a Compact Disc Read Only Memory(CD-ROM), and a CD-R (Recordable)/RW (ReWritable). Examples of themagneto-optical recording medium include a Magneto-Optical disk (MO).Note that the storing unit 1202 is also included in the non-transitoryrecording medium.

The input/output interface 1204 is connected to an input/output unit1208, and the input/output interface 1204 transmits information input bya user via the input/output unit 1208 to the control unit 1201 via thebus 1206. The input/output interface 1204 also transmits informationtransmitted from the control unit 1201 to the input/output unit 1208 viathe bus 1206.

The input/output unit 1208 corresponds to the image obtaining unit 2illustrated in FIG. 1 or 5, and examples of the input/output unit 1208include an imaging device. Examples of the input/output unit 1208 alsoinclude a keyboard, a pointing device (for example, a mouse), a touchpanel, a Cathode Ray Tube (CRT) display, and a printer.

The communication interface 1205 is an interface for performing LocalArea Network (LAN) connection or Internet connection. The communicationinterface 1205 may be used as an interface for performing LANconnection, Internet connection, or wireless connection with anothercomputer, as needed.

By using a computer having the hardware above, various processingfunctions performed by the biometrics authentication device 1 areimplemented. In this case, a computer executes a program describing thecontent of the various processing functions performed by the biometricsauthentication device 1 such that the above various processing functions(for example, the region specifying unit 3, the feature extracting unit4, the matching processing unit 5, and the score determining unit 6) areimplemented on the computer. The program describing the content of thevarious processing functions can be stored in the storing unit 1202 orthe recording medium 1207.

In a case in which a program is distributed, the recording medium 1207recording the program, such as a DVD or a CD-ROM, is sold separately,for example. The program can be recorded in a storage of a servercomputer, and the program can be transferred from the server computer toanother computer via a network.

The computer that executes a program stores, for example, the programrecorded in the recording medium 1207 or the program transferred fromthe server computer in the storing unit 1202. The computer reads theprogram from the storing unit 1202, and performs processing according tothe program. The computer may directly read a program from the recordingmedium 1207, and may perform processing according to the program.Further, every time a program is transferred from the server computer,the computer may perform processing according to the received program.

In the embodiments of the present disclosure, an image processing devicethat performs authentication using a vein of a palm has been describedas an example, but the embodiments are not limited to this, and anyother feature detection region of a living body may be used.

The other feature detection region of a living body is not limited to avein, and as an example may be a vascular image of a living body, apattern of a living body, a fingerprint or a palm print of a livingbody, the sole of the foot, a finger or toe, the back of the hand or theinstep of the foot, the wrist, the arm, or the like.

When the vein is used for authentication, the other feature detectionregion of a living body may be any region in which the vein can beobserved.

The existence of an other feature detection region of a living body inwhich biological information can be specified is advantageous toauthentication. As an example, when a palm, a face or the like is used,a region can be specified from an obtained image. In addition, variousmodifications to the embodiments above can be made without departingfrom the spirit of the embodiments. Further, multiple variations ormodifications to the embodiments above can be made by those skilled inthe art, and the embodiments are not limited to the accurateconfiguration and applications described above.

According to the embodiments of the present disclosure, even when amethod for physically separating a feature denoting a palm print from animage fails to be applied, an FAR can be prevented from increasing.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiments of the presentinvention have been described in detail, it should be understood thatthe various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. A biometrics authentication device comprising: afilter that extracts from an input image directional features thatrespectively correspond to directions different from each other; anperpendicular filter that, from among the directional features extractedfrom the filter, decreases a luminance value of the entirety of adirectional feature that corresponds to a prescribed direction,increases a luminance value of the entirety of a directional featurethat corresponds to a direction perpendicular to the directional featurethat corresponds to the prescribed direction, and outputs otherdirectional features with no change; a non-directional featuregeneration processing unit that generates a non-directional feature onthe basis of the directional features output from the perpendicularfilter; a matching processing unit that obtains a similarity between thenon-directional feature and a registered non-directional feature storedin a storing unit; and a determining unit that determines identity byusing the similarity.
 2. A biometrics authentication device comprising:a filter that extracts from an input image directional features thatrespectively correspond to directions different from each other; anon-directional feature generation processing unit that generates anon-directional feature on the basis of the directional featuresextracted from the filter; a selecting unit that selects a directionalfeature that corresponds to a prescribed direction from among thedirectional features extracted from the filter, and outputs thedirectional feature as a significant directional feature, and that alsoselects a directional feature that corresponds to a directionperpendicular to the significant directional feature from among thedirectional features extracted from the filter, and outputs thedirectional feature as an perpendicular directional feature; anon-directional feature matching processing unit that obtains a firstsimilarity between the non-directional feature and a registerednon-directional feature stored in a storing unit; a directional featurematching processing unit that obtains a second similarity between thesignificant directional feature and a registered significant directionalfeature stored in the storing unit, and that also obtains a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;a similarity adjusting unit that applies a smaller weight on the secondsimilarity than on the third similarity, and outputs a sum of the secondsimilarity and the third similarity after weighting as a fourthsimilarity; and a determining unit that determines identity by using thefirst similarity and the fourth similarity.
 3. A biometricsauthentication device comprising: a filter that extracts from an inputimage directional features that respectively correspond to directionsdifferent from each other; a non-directional feature generationprocessing unit that generates a non-directional feature on the basis ofthe directional features extracted from the filter; a selecting unitthat selects a directional feature that corresponds to a prescribeddirection from among the directional features extracted from the filter,and outputs the directional feature as a significant directionalfeature, and that also selects a directional feature that corresponds toa direction perpendicular to the significant directional feature fromamong the directional features extracted from the filter, and outputsthe directional feature as an perpendicular directional feature; anon-directional feature matching processing unit that obtains a firstsimilarity between the non-directional feature and a registerednon-directional feature stored in a storing unit; a directional featurematching processing unit that obtains a second similarity between thesignificant directional feature and a registered significant directionalfeature stored in the storing unit, and that also obtains a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;a similarity adjusting unit that applies a greater weight on the thirdsimilarity than on the second similarity, and outputs a sum of thesecond similarity and the third similarity after weighting as a fourthsimilarity; and a determining unit that determines identity by using thefirst similarity and the fourth similarity.
 4. A biometricsauthentication method comprising: extracting from an input image, by acomputer, directional features that respectively correspond todirections different from each other; from among the directionalfeatures that have been extracted, decreasing, by the computer, aluminance value of the entirety of a directional feature thatcorresponds to a prescribed direction, increasing a luminance value ofthe entirety of a directional feature that corresponds to a directionperpendicular to the directional feature that corresponds to theprescribed direction, and outputting other directional features with nochange; generating, by the computer, a non-directional feature on thebasis of the directional features that have been output; obtaining, bythe computer, a similarity between the non-directional feature and aregistered non-directional feature stored in a storing unit; anddetermining, by the computer, identity by using the similarity.
 5. Abiometrics authentication method comprising: extracting from an inputimage, by a computer, directional features that respectively correspondto directions different from each other; generating, by the computer, anon-directional feature on the basis of the directional features thathave been extracted; selecting, by the computer, a directional featurethat corresponds to a prescribed direction from among the directionalfeatures that have been extracted, and outputting the directionalfeature as a significant directional feature; selecting, by thecomputer, a directional feature that corresponds to a directionperpendicular to the significant directional feature from among thedirectional features that have been extracted, and outputting thedirectional feature as an perpendicular directional feature; obtaining,by the computer, a first similarity between the non-directional featureand a registered non-directional feature stored in a storing unit;obtaining, by the computer, a second similarity between the significantdirectional feature and a registered significant directional featurestored in the storing unit; obtaining, by the computer, a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;applying, by the computer, a smaller weight on the second similaritythan on the third similarity, and outputting a sum of the secondsimilarity and the third similarity after weighting as a fourthsimilarity; and determining, by the computer, identity by using thefirst similarity and the fourth similarity.
 6. A biometricsauthentication method comprising: extracting from an input image, by acomputer, directional features that respectively correspond todirections different from each other; generating, by the computer, anon-directional feature on the basis of the directional features thathave been extracted; selecting, by the computer, a directional featurethat corresponds to a prescribed direction from among the directionalfeatures that have been extracted, and outputting the directionalfeature as a significant directional feature; selecting, by thecomputer, a directional feature that corresponds to a directionperpendicular to the significant directional feature from among thedirectional features that have been extracted, and outputting thedirectional feature as an perpendicular directional feature; obtaining,by the computer, a first similarity between the non-directional featureand a registered non-directional feature stored in a storing unit;obtaining, by the computer, a second similarity between the significantdirectional feature and a registered significant directional featurestored in the storing unit; obtaining, by the computer, a thirdsimilarity between the perpendicular directional feature and aregistered perpendicular directional feature stored in the storing unit;applying, by the computer, a greater weight on the third similarity thanon the second similarity, and outputting a sum of the second similarityand the third similarity after weighting as a fourth similarity; anddetermining, by the computer, identity by using the first similarity andthe fourth similarity.
 7. A non-transitory computer-readable recordingmedium which records a program for causing a computer to execute aprocess comprising: extracting from an input image directional featuresthat respectively correspond to directions different from each other;from among the directional features that have been extracted, decreasinga luminance value of the entirety of a directional feature thatcorresponds to a prescribed direction, increasing a luminance value ofthe entirety of a directional feature that corresponds to a directionperpendicular to the directional feature that corresponds to theprescribed direction, and outputting other directional features with nochange; generating a non-directional feature on the basis of thedirectional features that have been output; obtaining a similaritybetween the non-directional feature and a registered non-directionalfeature stored in a storing unit; and determining identity by using thesimilarity.
 8. A non-transitory computer-readable recording medium whichrecords a program for causing a computer to execute a processcomprising: extracting from an input image directional features thatrespectively correspond to directions different from each other;generating a non-directional feature on the basis of the directionalfeatures that have been extracted; selecting a directional feature thatcorresponds to a prescribed direction from among the directionalfeatures that have been extracted, and outputting the directionalfeature as a significant directional feature; selecting a directionalfeature that corresponds to a direction perpendicular to the significantdirectional feature from among the directional features that have beenextracted, and outputting the directional feature as an perpendiculardirectional feature; obtaining a first similarity between thenon-directional feature and a registered non-directional feature storedin a storing unit; obtaining a second similarity between the significantdirectional feature and a registered significant directional featurestored in the storing unit; obtaining a third similarity between theperpendicular directional feature and a registered perpendiculardirectional feature stored in the storing unit; applying a smallerweight on the second similarity than on the third similarity, andoutputting a sum of the second similarity and the third similarity afterweighting as a fourth similarity; and determining identity by using thefirst similarity and the fourth similarity.
 9. A non-transitorycomputer-readable recording medium which records a program for causing acomputer to execute a process comprising: extracting from an input imagedirectional features that respectively correspond to directionsdifferent from each other; generating a non-directional feature on thebasis of the directional features that have been extracted; selecting adirectional feature that corresponds to a prescribed direction fromamong the directional features that have been extracted, and outputtingthe directional feature as a significant directional feature; selectinga directional feature that corresponds to a direction perpendicular tothe significant directional feature from among the directional featuresthat have been extracted, and outputting the directional feature as anperpendicular directional feature; obtaining a first similarity betweenthe non-directional feature and a registered non-directional featurestored in a storing unit; obtaining a second similarity between thesignificant directional feature and a registered significant directionalfeature stored in the storing unit; obtaining a third similarity betweenthe perpendicular directional feature and a registered perpendiculardirectional feature stored in the storing unit; applying a greaterweight on the third similarity than on the second similarity, andoutputting a sum of the second similarity and the third similarity afterweighting as a fourth similarity; and determining identity by using thefirst similarity and the fourth similarity.